基于改进忆阻细胞神经网络的彩色图像边缘提取

Color image edge extraction using memristor-based CNN

细胞神经网络具有局部互联结构和高速并行处理的能力,被广泛应用于图像处理.然而,一方面,现有处理方法大多采用固定模板,在处理实际复杂图像时难以得到较好的效果.另一方面,因传统CMOS工艺发展瓶颈,利用其实现大规模细胞神经网络变得不切实际.本文首先以人眼感知原理为基础,考虑图像中各个像素空间分布的影响,提出了一种新型阈值自适应算法,其克服了传统边缘提取算法的局限性.利用具有独特开关转换机制、非易失性和纳米级尺寸等优点的新型非线性两端电路元件忆阻器来解决CNN的硬件实现难题.将自旋忆阻器与细胞神经网络相结合形成忆阻细胞神经网络作为算法的硬件.最后,对彩色图像的边缘提取进行数值仿真,抗噪性检测,与传统边缘提取算法对比分析,并计算各算法边缘提取结果的FOM(figure of merit)值和峰值信噪比(PSNR),验证了基于像素空间分布的阈值自适应忆阻细胞神经网络在彩色图像边缘提取中的有效性.

Because of the locally connected lattice structure and high-speed parallel processing, cellular neural network（CNN） have been widely used in image processing. Traditional processing methods typically employ fixed templates, which impose significant limitations on practical complex image processing. However, the hardware implementation of large-scale CNNs becomes impossible due to the bottleneck of traditional CMOS technology.In this paper, a new threshold-adaptive algorithm is proposed by considering pixel space distributions based on human visual perception, which can overcome the aforementioned limitation. Then, the memristor, a twoterminal nonlinear device with unique high-speed switching, nonvolatility, and nanometer scale is used to solve the circuit realization problem. Specifically, we design a spintronic memristor-based CNN（SMCNN） to facilitate the proposed threshold-adaptive algorithm. Finally, by using the example of color image processing, the effectiveness of the proposed SMCNN is demonstrated by means of numerical simulations and comparative analysis.